In today’s digital age, leading organizations are looking for better ways to get more out of their data. They are choosing platforms that make every employee more connected, productive, and mobile-without compromising security. As companies adopt Box, providing intuitive information access and advanced search capabilities become increasingly important to end users. Using advanced Natural Language Processing (NLP) and Machine Learning algorithms, Sinequa’s Cognitive Search & Analytics platform enables users to search, analyze and gain valuable insights extracted from Box content repositoriesalong with on-premises enterprise applications, big data and cloud environments.

To build a sophisticated search and analytics engine is one thing, but to build such an engine that can preserve all the native security and permissions settings of connected repositories is another matter altogether. With Sinequa and Box connected, workers can search the Box environment (and all other data sources) while maintaining the native control settings of the respective platforms in which the data resides. This ensures that the granular security and permissions within Box are maintained in the Sinequa search interface, allowing individual users to seamlessly search and leverage only the content they are entitled to access.

The result is an environment unhindered by unnecessary, cumbersome processes for permission requests, or worse, unintentional viewing of unauthorized content. This allows users to quickly search and pinpoint the data, content, subject-matter experts, and topics they need in a fully secure and managed environment, where only the relevant data appears to each individual.
To learn more about the partnership between Box.com and Sinequa and the benefits of Cognitive Search & Analytics, you can download the complimentary research note “Sinequa partnership with Box amplifies cross-platform enterprise search and analytics” – April 2017 – from Paige Bartley, Senior Analyst at Ovum.

Despite the effort from technology vendors to deliver relevant, contextual, and actionable insights with their applications, most organizations have been slow if not reluctant to embrace these advances in search-driven experiences. In fact, a lot of companies have been burned by their past enterprise search experiences.

The good news is that something is shaking the world of Enterprise Search – some would say ‘finally.’ New industry investments and R&D effort are changing the search experience to provide more relevant results and deeper insights to users in their work context.

As we enter the era of “cognitive computing,” new search solutions combine powerful indexing technology with advanced Natural Language Processing (NLP) capabilities and Machine Learning algorithms in order to build an increasingly deep corpus of knowledge from which to feed relevant information and 360° views to users in real-time. This is what leading analyst firms call “Cognitive Search” or “Insight Engines.”These cognitively-enabled platforms interact with users in a more natural fashion, learn/progress as they gain more experience with data and user behavior, and proactively establish links between related data from various sources, both internal and external.

“Indexing, natural language processing, and machine-learning technologies combined to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.”

How does cognitive search work to deliver relevant knowledge?

It extracts valuable information from large volumes of complex and diverse data sources. It is crucial to tap into all available enterprise data whether internal or external, both structured and unstructured, to provide deeper insights to users in order for them to make better business decisions. Cognitive search provides this connection to provide comprehensive insights.

It provides contextually and relevant information. Finding relevant knowledge across all available enterprise data requires cognitive systems using Natural Language Processing (NLP) capable of “understanding” what unstructured data from texts (documents, emails, social media blogs, engineering reports, market research…), and rich-media content (videos, call center recordings..), is about. Machine Learning algorithms help refine the insight gained from data. Trade and company dictionaries and ontologies help with synonyms and with relationships between different terms and concepts. That means a lot of intelligence and horse power “under the hood” of a system providing “relevant knowledge” or insight.

It leverages Machine Learning Capabilities to continuously improve the results relevancy. Machine Learning algorithms (amongst the most popular ones: Collaborative Filtering and Recommendations, Classification by Example, Clusterization, Similarity calculations for unstructured contents, and Predictive Analysis) provide added value by continuously refining and enhancing the search results in an effort to provide the best relevancy to users.

Thanks to new technology advancements, cognitive search brings to data-driven organizations a new generation of search enabling them to go far beyond the traditional search box, empowering its users to get immediate and relevant knowledge at the right time on the right device.

Big Data. It’s among the most pressing challenges — and opportunities — for today’s solution providers. Enterprise data, be it structured in databases and enterprise applications or unstructured textual data from documents (including contracts, letters, emails, news-feeds, websites, and more) or videos and images, contains a wealth of content that, if searched and analyzed with cognitive intelligence, can deliver valuable insights for the customers you serve.

It’s common today to have numerous silos, both on premise and in the cloud, of content in which critical data resides. From customer records and contracts to financial data and emails, data silos often take many different shapes and forms without the ability to “talk” to one another. If only a 360 degree view of this data were available at the employees’ finger tips. This could provide deeper customer insight, increased sales opportunities, and greater customer loyalty with the ability to meet rapidly evolving customer expectations.

With cognitive search and analytics, this goal can be achieved. Leveraging Machine Learning algorithms and advanced natural language processing (NLP), cognitive search and analytics solutions enable customers to embark on ambitious Big Data projects with the opportunity to extract relevant information from the volumes of content they retain.

In fact, some search and analytics solutions even offer as many as 150 smart connectors, out of the box that can seamlessly connect to multiple sources of data. This works to integrate your customers’ industry specific dictionaries allowing the information to be indexed, putting their specific knowledge under the hood of one platform — making it an intelligent partner for anyone searching for relevant information for his/her subject.

To efficiently leverage Big Data for your customers, consider an advanced search and analytics platform that delivers these five critical elements.

Cognitive search with a combination of indexing, natural language processing and machine learning. For a search and analytics solution to be effective, it needs to understand the natural language as it’s spoken across ever major language. This will help to deal with unstructured content such as email and document files. It should also leverage machine learning algorithms so that it can learn as it progresses, delivering more value and insight with each new volume it analyzes. This is what one analyst firm defines as Cognitive Search which allows organizations to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.

Extensive connections for comprehensive indexing. To make the most of the multiple silos of data throughout the organization, a search and analytics solution needs to have a wide range of connectors so that it can support every type of data, making it easily ingested into the platform so that it’s included in a comprehensive analysis. Building connectors before starting projects will delay value extraction and make projects more expensive. From databases and enterprise applications including CRM and ERP systems such as SAP, to big data Hadoop environments, cloud applications like Office 365, GoogleApps and Salesforce, and cloud storage such as Box and Microsoft OneDrive, having a connector for every vital application in the business will ensure that the resulting insights deliver a complete view into the business.

Support for the structured and unstructured. When analyzing business data, it’s critical to include unstructured data, such as email and document files, as well as structured content, like the data included in databases. Only when both are included in an analysis can true insights be revealed. Since so much valuable data is embedded in unstructured files, evaluating these contents can produce truly insightful information into the business that can’t otherwise be recognized by evaluating structured forms of content.

Extensive security and access control. Today’s data is not only a critical asset, it’s also private and stringently regulated. Any solution that touches regulated data must follow strict security and compliance guidelines, ensuring that policy controls are in place. Be sure to select a search and analytics platform that supports stringent access controls, including user authentication, cross-domain security and secure communications, to assure that compliance practices are followed.

Agility to support hybrid infrastructure. The cloud is quickly changing everything. When large data sets are in play, it’s very likely that much of that data is being retained in cloud-based environments. Whether retained in public cloud solutions, such as Amazon Web Services (AWS), or private cloud architectures, data still must be accessed and integrated into a comprehensive enterprise search and analytics solution to be part of a successful solution for true business insights. Here it’s critical to select a solution that will not only support any combination of a private and public cloud infrastructure as well as on-premises architectures with a hybrid approach to data analysis that will also support hundreds of millions of documents and billions of database records. This will ensure that regardless of how large the environment becomes, and wherever data may reside, it can become part of a comprehensive analysis for true and accurate results.

Big data presents a wealth of opportunity for you and your customers. By taking a holistic approach to cognitive search and analytics so that every silo of data is included in an enterprise search activity, the insights can be exceptionally revealing. These results can not only increase customer opportunities, grow sales and improve overall organizational productivity, they’ll also help you build the customer loyalty that will pay off for years to come.